Text coherence evaluation becomes a vital and lovely task in Natural Language Processing subfields, such as text summarization, question answering, text generation and machine translation. Existing methods like entity-based and graph-based models are engaging with nouns and noun phrases change role in sequential sentences within short part of a text. They even have limitations in global coherence evaluation, especially in long and narrative documents. This paper presents a new and simplified method for evaluating local and global text coherence. The proposed method focuses on entity grid method and employs two graph-based and entropy-based approaches to overcome its challenges and shortcomings. Applying statistical approaches, the presented method studies how to incorporate other entity properties into short and long stories to assess both local and global coherence, simultaneously. Results indicate that the proposed method is superior to other algorithms in terms of performance, accuracy in long documents with a high number of sentences. Manuscript profile